Image processing method and image processing device

Abstract

To correctly determine the cause of a noise point being one type of an image defect based on a test image.SOLUTION: A processor 80 creates an extraction image g23 obtained by extracting a noise point Ps13 being one type of an image defect in a test image g1. In addition, the processor 80 determines the cause of the noise point Ps13 by using at least one of an edge intensity of the noise point Ps13 in the extraction image g23, the degree of flatness of the noise point Ps13, and a pixel value distribution in a traversal pixel column AR1 being a pixel column traversing the noise point Ps13.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to an image processing method and an image processing apparatus for determining the cause of an image defect based on a test image. [Background technology]

[0002] 2. Description of the Related Art Image forming apparatuses such as printers or multifunction peripherals execute a printing process to form an image on a sheet. In the printing process, image defects such as vertical streaks, horizontal streaks, or noise dots may occur in the image formed on the output sheet.

[0003] For example, when the image forming apparatus is an apparatus that executes the printing process by an electrophotographic method, the cause of the image defect is considered to be various parts such as a photoconductor, a charging unit, a developing unit, a transfer unit, etc. Furthermore, skill is required to determine the cause of the image defect.

[0004] It is also known that in an image processing device, a phenomenon that causes vertical streaks, which is an example of an image defect, and characteristic information such as the color, density or screen line number of the vertical streaks are previously associated as table data, and the phenomenon that causes the vertical streaks is identified based on the information on the color, density or screen line number of the image of the vertical streaks in a test image and the table data (see, for example, Patent Document 1).

[0005] The table data is data in which ranges of parameters such as image color, density, screen ruling, etc. are set by threshold values ​​for each type of phenomenon that causes the vertical stripes. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2017-83544 A Summary of the Invention [Problem to be solved by the invention]

[0007] Incidentally, noise spots, which are one type of image defect, can occur due to various causes such as abnormal development, carrier development, or waste toner dropping.

[0008] The abnormal development is a phenomenon in which a dot-like defective toner image is developed on the surface of the photoconductor by the developing unit of the image forming apparatus and then transferred to a sheet. The carrier development is a phenomenon in which a magnetic carrier mixed with the toner in a two-component developer moves to the photoconductor and then is transferred to a sheet.

[0009] The waste toner falling is a phenomenon in which waste toner adhering to an intermediate transfer belt that transfers a toner image onto a sheet is transferred onto the sheet. The waste toner is toner that returns to the intermediate transfer belt from a belt cleaning device that removes residual toner from the intermediate transfer belt.

[0010] Correctly determining the cause of the noise points is difficult unless one is an expert, and measures to eliminate the noise points differ depending on the cause of the noise points.

[0011] An object of the present invention is to provide an image processing method and an image processing apparatus that can correctly determine the cause of noise points, which are a type of image defect, based on a test image obtained through an image reading process for an output sheet of an image forming apparatus. [Means for solving the problem]

[0012] An image processing method according to one aspect of the present invention is a method in which a processor determines a cause of an image defect based on a test image obtained through an image reading process for an output sheet of an image forming apparatus. The image processing method includes the processor generating an extracted image in which a noise point, which is a type of the image defect, is extracted from the test image. The image processing method further includes the processor determining a cause of the noise point using at least one of an edge strength of the noise point in the extracted image, a degree of flattening of the noise point, and a pixel value distribution in a transverse pixel column, which is a pixel column that crosses the noise point.

[0013] An image processing device according to another aspect of the present invention includes a processor that executes the process of the image processing method. Effect of the Invention

[0014] According to the present invention, it is possible to provide an image processing method and an image processing device that can correctly determine the cause of noise points, which are a type of image defect, based on a test image obtained through an image reading process for an output sheet of an image forming device. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 is a configuration diagram of an image processing apparatus according to an embodiment. [Diagram 2] FIG. 2 is a block diagram showing the configuration of a data processing unit in the image processing device according to the embodiment. [Diagram 3] FIG. 3 is a flowchart showing an example of a procedure of an image defect determination process in the image processing device according to the embodiment. [Figure 4] FIG. 4 is a flowchart illustrating an example of a procedure of the specific defect determination process in the image processing device according to the embodiment. [Diagram 5] FIG. 5 is a flowchart showing an example of a procedure of the noise point determination process in the image processing device according to the embodiment. [Figure 6]FIG. 6 is a flowchart showing an example of a procedure of the voltage correction process in the image processing device according to the embodiment. [Figure 7] FIG. 7 is a diagram showing an example of a test image including a singular portion, and an example of a preprocessed image and a feature image generated based on the test image. [Figure 8] FIG. 8 is a diagram showing an example of a region of interest and adjacent regions sequentially selected from a test image in the main filtering process by the image processing device according to the embodiment. [Figure 9] 9A and 9B are diagrams showing an example of a noise point, a pixel value distribution in a traversing pixel column, and a differential value distribution in the traversing pixel column, where FIG. 9A shows a case where the noise point symptoms are mild, FIG. 9B shows a case where the noise point symptoms are moderate, and FIG. 9C shows a case where the noise point symptoms are severe. [Figure 10] FIG. 10 is a diagram showing a line screen, which is an example of an original drawing portion in a test image. [Figure 11] FIG. 11 is a diagram showing an example of the positional relationship between noise points and original drawing parts in a test image. [Figure 12] FIG. 12 is a flowchart showing an example of a procedure of a feature image generation process in the first application example of the image processing device according to the embodiment. [Figure 13] FIG. 13 is a flowchart showing an example of a procedure of feature image generation processing in the second application example of the image processing device according to the embodiment. [Figure 14] FIG. 14 is a flowchart showing an example of a procedure of feature image generation processing in the third application example of the image processing device according to the embodiment. [Figure 15] FIG. 15 is a flowchart showing an example of a procedure of feature image generation processing in the fourth application example of the image processing device according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the following embodiment is an example of the present invention, and is not intended to limit the technical scope of the present invention.

[0017] [Configuration of image processing device 10] As shown in Fig. 1, an image processing device 10 according to an embodiment includes an image forming device 2 that executes a print process. The print process is a process for forming an image on a sheet. The sheet is an image forming medium such as paper or a sheet-like resin member.

[0018] The image processing device 10 further includes an image reading device 1 that executes a reading process for reading an image from a document. For example, the image processing device 10 is a copying machine, a facsimile machine, a multifunction machine, or the like.

[0019] In addition to the image forming device 2 and the image reading device 1, the image processing device 10 includes a data processing unit 8 and a human interface device 800. The human interface device 800 includes an operation unit 801 and a display unit 802.

[0020] The image to be printed is an image read from the document by the image reading device 1 or an image represented by print data received from a host device (not shown), etc. The host device is an information processing device such as a personal computer or a mobile information terminal.

[0021] Furthermore, the image forming apparatus 2 may form a predetermined original test image g01 on the sheet by the print process (see FIG. 7). The original test image g01 is an image that is the basis of a test image g1 that is used to determine the presence or absence of an image defect and its cause in the image forming apparatus 2 (see FIG. 7). The test image g1 will be described later.

[0022] The process including the reading process by the image reading device 1 and the print process by the image forming device 2 based on the image obtained by the reading process is a copy process.

[0023] 1, the image forming apparatus 2 includes a sheet transport mechanism 3 and a print unit 4. The sheet transport mechanism 3 includes a sheet sending mechanism 31 and a plurality of pairs of sheet transport rollers 32.

[0024] The sheet sending mechanism 31 sends out the sheet from the sheet storage unit 21 to the sheet transport path 30. A plurality of sheet transport roller pairs 32 transport the sheet along the sheet transport path 30 and discharge the sheet on which an image has been formed onto the discharge tray 22.

[0025] The printing unit 4 performs the printing process on the sheet transported by the sheet transport mechanism 3. In this embodiment, the printing unit 4 performs the printing process by electrophotography.

[0026] The print section 4 includes an image forming section 4x, a bias output circuit 430, a laser scanning unit 4y, a transfer device 44, and a fixing device 46. The image forming section 4x includes a drum-shaped photoconductor 41, a charging device 42, a developing device 43, and a drum cleaning device 45.

[0027] The photoreceptor 41 rotates, and the charging device 42 uniformly charges the surface of the photoreceptor 41. The charging device 42 includes a charging roller 42a that rotates in contact with the surface of the photoreceptor 41. The charging device 42 charges the surface of the photoreceptor 41 by outputting a charging voltage to the photoreceptor 41 through the charging roller 42a.

[0028] The laser scanning unit 4y writes an electrostatic latent image on the charged surface of the photoconductor 41 by scanning with a laser beam.

[0029] The developing device 43 develops the electrostatic latent image into a toner image. The photoconductor 41 is an example of an image carrier that carries the toner image. The developing device 43 includes a developing roller 43a, a developing tank 43b, and a regulating blade 43c. The developing tank 43b is a container that contains toner.

[0030] The developing roller 43a rotates while carrying the toner in the developing tank 43b, thereby supplying the toner to the surface of the photoconductor 41. The bias output circuit 430 applies a developing bias to each of the developing rollers 43a. The reference potential of the developing bias is the potential of the photoconductor 41. In this embodiment, the bias output circuit 430 is capable of correcting the developing bias.

[0031] The regulating blade is a member that limits the thickness of the layer of the toner carried on the developing roller 43a. Each of the four developing devices 43 corresponding to the four types of developing colors is an example of a developing unit.

[0032] The transfer device 44 transfers the toner image on the surface of the photoconductor 41 onto the sheet. The toner is an example of a granular developer.

[0033] The fixing device 46 heats the toner image on the sheet to fix it to the sheet. The fixing device 46 includes a fixing rotor 46a that rotates in contact with the sheet, and a fixing heater 46b that heats the fixing rotor 46a.

[0034] The image forming apparatus 2 shown in Fig. 1 is a tandem color printing apparatus and is capable of executing the printing process of a color image. Therefore, the printing unit 4 has four image forming units 4x, each of which corresponds to a different color toner. Each of the four image forming units 4x develops a different color.

[0035] In the tandem image forming apparatus 2, the transfer device 44 includes four primary transfer rollers 441 corresponding to the four photoconductors 41, an intermediate transfer belt 440, a secondary transfer roller 442, and a belt cleaning device 443.

[0036] The four image forming units 4x respectively form the cyan, magenta, yellow and black toner images on the surface of the photoconductor 41. Each of the primary transfer rollers 441 is also a part of each of the image forming units 4x.

[0037] In each of the image forming units 4x, the primary transfer roller 441 rotates and presses the intermediate transfer belt 440 against the surface of the photoconductor 41. The primary transfer roller 441 transfers the toner image from the photoconductor 41 to the intermediate transfer belt 440. As a result, a color image made up of the four color toner images is formed on the intermediate transfer belt 440.

[0038] In each of the image forming units 4 x , the drum cleaning device 45 removes and collects from the photoconductor 41 the toner that has not been transferred to the intermediate transfer belt 440 and remains on the photoconductor 41 .

[0039] The secondary transfer roller 442 transfers the four-color toner image on the intermediate transfer belt 440 onto the sheet. In the image processing device 10, the intermediate transfer belt 440 is an example of a transfer body that transfers the toner image onto the sheet.

[0040] The belt cleaning device 443 removes and collects from the intermediate transfer belt 440 the toner that has not been transferred to the sheet and remains on the intermediate transfer belt 440 .

[0041] The data processing unit 8 executes various data processing operations related to the printing process or the reading process, and further controls various electrical devices.

[0042] The operation unit 801 is a device that accepts operations by a user. For example, the operation unit 801 includes one or both of a push button and a touch panel. The display unit 802 includes a display panel that displays information to be provided to the user.

[0043] As shown in FIG. 2, the data processing unit 8 includes a central processing unit (CPU) 80, a random access memory (RAM) 81, a secondary storage device 82, and a communication device 83.

[0044] The CPU 80 is capable of processing data received by the communication device 83, various types of image processing, and controlling the image forming device 2. The received data may include the print data. The CPU 80 is an example of a processor that executes data processing including the image processing. The CPU 80 may be realized by another type of processor, such as a DSP (Digital Signal Processor).

[0045] The communication device 83 is a communication interface device that communicates with other devices such as the host device through a network such as a LAN (Local Area Network). The CPU 80 performs all data transmission and reception between the CPU 80 and the external devices through the communication device 83.

[0046] The secondary storage device 82 is a computer-readable non-volatile storage device. The secondary storage device 82 stores the computer programs executed by the CPU 80 and various data referenced by the CPU 80. For example, one or both of a flash memory and a hard disk drive are adopted as the secondary storage device 82.

[0047] The RAM 81 is a computer-readable volatile storage device that temporarily stores the computer programs executed by the CPU 80 and data output and referenced in the process of the CPU 80 executing the programs.

[0048] The CPU 80 includes a plurality of processing modules that are realized by executing a computer program. The plurality of processing modules include a main control unit 8a and a job control unit 8b. Note that some or all of the plurality of processing modules may be realized by a processor independent of the CPU 80, such as a DSP.

[0049] The main control unit 8a executes a process of selecting a job in response to an operation on the operation unit 801, a process of displaying information on the display unit 802, a process of setting various data, etc. Furthermore, the main control unit 8a also executes a process of determining the contents of data received by the communication device 83.

[0050] The job control unit 8b controls the image reading device 1 and the image forming device 2. For example, when the received data of the communication device 83 includes the print data, the job control unit 8b causes the image forming device 2 to execute the received print process.

[0051] In addition, when the main control unit 8a detects a copy request operation on the operation unit 801, the job control unit 8b causes the image reading device 1 to execute the reading process, and causes the image forming device 2 to execute the printing process based on the image obtained by the reading process.

[0052] In the printing process, image defects such as vertical streaks Ps11, horizontal streaks Ps12, or noise spots Ps13 may occur in the image formed on the output sheet (see FIG. 7).

[0053] As described above, the image forming apparatus 2 executes the print process by electrophotography. In this case, the cause of the image defect is considered to be various parts such as the photoconductor 41, the charging device 42, the developing device 43, and the transfer device 44. Skill is required to determine the cause of the image defect.

[0054] In this embodiment, the image forming apparatus 2 executes a test print process for forming a predetermined original test image g01 on the sheet (see FIG. 7).

[0055] For example, when the main control unit 8a detects a test output operation on the operation unit 801, the job control unit 8b causes the image forming device 2 to execute the test print process. In the following description, the sheet on which the original test image g01 is formed is referred to as a test output sheet 9 (see FIG. 1).

[0056] Furthermore, when the test print process is executed, the main control unit 8a causes the display unit 802 to display a predetermined guidance message. This guidance message is a message that prompts the user to set the test output sheet 9 in the image reading device 1 and then to perform a reading start operation on the operation unit 801.

[0057] Then, when the main control unit 8a detects a reading start operation on the operation unit 801 after the guidance message is displayed on the display unit 802, the job control unit 8b causes the image reading device 1 to execute the reading process. As a result, the original test image g01 is read by the image reading device 1 from the test output sheet 9 output by the image forming device 2, and a read image corresponding to the original test image g01 is obtained.

[0058] Then, as described later, the CPU 80 executes a process for determining the presence or absence of the image defect and its cause based on a test image g1 which is the read image or an image obtained by compressing the read image (see FIG. 6). The CPU 80 is an example of a processor that executes the process of an image processing method for determining the presence or absence of the image defect and its cause.

[0059] The device that reads the original test image g01 from the test output sheet 9 may be, for example, a digital camera. The process in which the image reading device 1 or the digital camera reads the original test image g01 from the test output sheet 9 is an example of an image reading process for the test output sheet 9.

[0060] Incidentally, the noise point Ps13, which is one type of image defect, can occur due to various causes such as abnormal development, carrier development, or waste toner falling.

[0061] The abnormal development is a phenomenon in which a dot-like defective toner image is developed on the surface of the photoconductor 41 by the developing device 43 of the image forming apparatus 2, and is then transferred to the sheet.

[0062] The carrier development is a phenomenon in which the magnetic carrier mixed with the toner in the two-component developer migrates to the photoreceptor 41 and is then transferred to the sheet. The magnetic carrier may migrate from the developing roller 43a to the surface of the photoreceptor 41 due to factors such as the surrounding environment or deterioration of the magnetic carrier. In this case, the magnetic carrier is transferred from the surface of the photoreceptor 41 to the sheet by a transfer device 44. In general, the color of the magnetic carrier is black.

[0063] The waste toner drop is a phenomenon in which waste toner adhering to intermediate transfer belt 440, which transfers a toner image onto the sheet, is transferred onto the sheet. The waste toner is toner that returns to intermediate transfer belt 440 from belt cleaning device 443, which removes remaining toner from intermediate transfer belt 440. The waste toner may be temporarily collected by belt cleaning device 443 and then transferred to intermediate transfer belt 440. In general, the color of the waste toner is a mixture of a plurality of developing colors.

[0064] It is difficult for anyone other than an expert to correctly determine the cause of the noise point Ps13. Also, measures to eliminate the noise point Ps13 differ depending on the cause of the noise point Ps13.

[0065] The CPU 80 executes an image defect determination process described later, thereby correctly determining the cause of the noise point Ps13, which is one type of image defect, based on the test image g1 obtained through the image reading process on the test output sheet 9.

[0066] In the following description, images such as the test image g1 to be processed by the CPU 80 are digital image data. The digital image data constitutes map data including a plurality of pixel values ​​corresponding to two-dimensional coordinate regions in a main scanning direction D1 and a sub-scanning direction D2 intersecting the main scanning direction D1 for each of the three primary colors. The three primary colors are, for example, red, green, and blue. The sub-scanning direction D2 is perpendicular to the main scanning direction D1. The main scanning direction D1 is the horizontal direction in the test image g1, and the sub-scanning direction D2 is the vertical direction in the test image g1.

[0067] In this embodiment, the test image g1 is a mixed-color halftone image formed by combining a plurality of uniform monochromatic halftone images corresponding to a plurality of developed colors in the image forming apparatus 2. The monochromatic halftone images are each uniformly formed at a predetermined intermediate gradation reference density.

[0068] For example, the original test image g01 and the test image g1 are mixed-color halftone images formed by combining a plurality of uniform monochromatic halftone images corresponding to a plurality of developed colors in the image forming apparatus 2. The monochromatic halftone images are each uniformly formed at a predetermined intermediate gradation reference density.

[0069] In this embodiment, the original test image g01 and the test image g1 are mixed-color halftone images obtained by combining four uniform single-color halftone images corresponding to all the developed colors in the image forming apparatus 2. In the test print process, one test output sheet 9 including one original test image g01 is output. Therefore, one test image g1 corresponding to the original test image g01 is the target for identifying the image defect. The test image g1 in this embodiment is an example of a mixed-color test image.

[0070] In addition, the multiple processing modules in CPU 80 further include a feature image generation unit 8c, a singular part identification unit 8d, a color vector identification unit 8e, a periodicity determination unit 8f, a pattern recognition unit 8g and a noise point determination unit 8h in order to execute the image defect judgment process (see Figure 2).

[0071] [Image defect judgment processing] An example of the procedure of the image defect determination process will be described below with reference to the flowchart shown in Fig. 3. In the following description, S101, S102, ... represent identification codes of a plurality of steps in the image defect determination process.

[0072] After the guidance message is displayed on the display unit 802, when the reading process is executed in response to the reading start operation on the operation unit 801, the main control unit 8a causes the characteristic image generation unit 8c to execute the processing of step S101 in the image defect judgment processing.

[0073] <Process S101> In step S101, the characteristic image generating unit 8c generates a test image g1 from the read image obtained in the image reading process for the test output sheet 9.

[0074] For example, the characteristic image generating unit 8c extracts a portion of the original image excluding the outer margin area from the read image as a test image g1.

[0075] Alternatively, the characteristic image generating unit 8c generates a test image g1 by a compression process that compresses the portion of the original image excluding the outer margin area from the scanned image to a predetermined standard resolution. The characteristic image generating unit 8c compresses the scanned image when the resolution of the scanned image is higher than the standard resolution. After generating the test image g1, the main control unit 8a shifts the process to step S102.

[0076] <Process S102> In step S102, the characteristic image generating unit 8c starts a peculiar defect determination process, which will be described later. The peculiar defect determination process is a process for determining the presence or absence of a peculiar part Ps1, such as a vertical streak Ps11, a horizontal streak Ps12, or a noise point Ps13, in the test image g1, and the cause of the occurrence of the peculiar part Ps1 (see FIG. 7). The peculiar part Ps1 is an example of the image defect.

[0077] Furthermore, when the specific defect determination process is completed, the main control unit 8a shifts the process to step S103.

[0078] <Process S103> In step S103, if it is determined in the process of step S102 that the image defect has occurred, the main control unit 8a shifts the process to step S104, and if not, shifts the process to step S105.

[0079] <Process S104> In step S104, the main control unit 8a executes a defect handling process that is associated in advance with the type and cause of the image defect that has been determined to have occurred in the process of step S102.

[0080] For example, the defect response process includes one or both of a first response process and a second response process shown below. The first response process is a process for displaying a message indicating a result of determining the cause of the image defect and a measure to address the cause of the image defect on the display unit 802.

[0081] When the cause of the image defect includes the cause of the noise point Ps13, the first response process is an example of a process of notifying the determination result of the noise point Ps13 through a notification device. The display unit 802 is an example of the notification device. The message indicating the countermeasure is, for example, a message encouraging replacement or cleaning of a part corresponding to the cause of the image defect.

[0082] The second response process is a process of correcting an image forming parameter to eliminate or mitigate the image defect. The image forming parameter is a parameter related to control of the image forming unit 4x. In this embodiment, when it is determined that the noise point Ps13 is caused by the abnormal development, the main control unit 8a executes a voltage correction process, which will be described later, as the second response process.

[0083] After executing the defect handling process, the main control unit 8a ends the image defect determination process.

[0084] <Process S105> On the other hand, in step S105, the main control unit 8a issues a normal notification indicating that the image defect has not been identified, and then ends the image defect determination process.

[0085] [Specific defect judgment processing] Next, an example of the procedure of the specific defect determination process in step S102 will be described with reference to the flowchart shown in Fig. 4. In the following description, S201, S202, ... represent identification codes of multiple steps in the specific defect determination process. The specific defect determination process starts from step S201.

[0086] <Process S201> First, in step S201, the feature image generating unit 8c generates a plurality of feature images g21, g22, and g23 by executing a predetermined feature extraction process on the test image g1. Each of the feature images g21, g22, and g23 is an image in which a specific type of singular part Ps1 of the test image g1 is extracted.

[0087] In this embodiment, the multiple characteristic images g21, g22, and g23 include a first characteristic image g21, a second characteristic image g22, and a third characteristic image g23 (see FIG. 7).

[0088] The first characteristic image g21 is an image in which the vertical streaks Ps11 in the test image g1 are extracted. The second characteristic image g22 is an image in which the horizontal streaks Ps12 in the test image g1 are extracted. The third characteristic image g23 is an image in which the noise points Ps13 in the test image g1 are extracted.

[0089] In this embodiment, the feature extraction process includes a first pre-processing process, a second pre-processing process, and a singular part extraction process. In the following description, a pixel sequentially selected from the test image g1 is referred to as a pixel of interest Px1 (see FIGS. 7 and 8).

[0090] The feature image generating unit 8c generates a first preprocessed image g11 by executing the first preprocessing on the test image g1 with the main scanning direction D1 as the processing direction Dx1 (see FIG. 5).

[0091] Furthermore, the feature image generating unit 8c generates a second preprocessed image g12 by executing the second preprocessing on the test image g1 with the sub-scanning direction D2 as the processing direction Dx1 (see FIG. 5).

[0092] Furthermore, the feature image generating unit 8c generates three feature images g21, g22, and g23 by executing the singular part extraction process on the first preprocessed image g11 and the second preprocessed image g12.

[0093] The first pre-processing includes a main filtering process in which the main scanning direction D1 is set as a processing direction Dx1. The main filtering process converts the pixel values ​​of the target pixels Px1 selected sequentially from the test image g1 into conversion values ​​obtained by processing that emphasizes the difference between the pixel values ​​of the target area Ax1 and the pixel values ​​of two adjacent areas Ax2 (see FIGS. 5 and 6).

[0094] The region of interest Ax1 is a region that includes the pixel of interest Px1, and the two adjacent regions Ax2 are regions that are adjacent to both sides of the region of interest Ax1 in a processing direction Dx1 that is set in advance. Each of the region of interest Ax1 and the adjacent region Ax2 is a region that includes one or more pixels.

[0095] The sizes of the attention area Ax1 and the adjacent area Ax2 are set according to the width of the vertical streaks Ps11 or horizontal streaks Ps12 to be extracted, or the size of the noise points Ps13 to be extracted.

[0096] Each of the region of interest Ax1 and the adjacent region Ax2 occupies the same range in a direction intersecting the processing direction Dx1. In the example shown in FIG. 6, the region of interest Ax1 is an area of ​​21 pixels across 3 columns and 7 rows with the pixel of interest Px1 at the center. Each of the adjacent regions Ax2 is also an area of ​​21 pixels across 3 columns and 7 rows. Note that in each of the region of interest Ax1 and the adjacent region Ax2, the number of rows is the number of lines along the processing direction Dx1, and the number of columns is the number of lines along the direction intersecting the processing direction Dx1. The size of each of the region of interest Ax1 and the adjacent region Ax2 is set in advance.

[0097] In the main filter process, each pixel value of the region of interest Ax1 is converted to a first correction value using a predetermined first correction coefficient K1, and each pixel value of each adjacent region Ax2 is converted to a second correction value using a predetermined second correction coefficient K2.

[0098] For example, the first correction coefficient K1 is a coefficient of 1 or more by which each pixel value in the region of interest Ax1 is multiplied, and the second correction coefficient K2 is a coefficient of less than 0 by which each pixel value in the adjacent region Ax2 is multiplied. In this case, the first correction coefficient K1 and the second correction coefficient K2 are set so that the sum of the value obtained by multiplying the first correction coefficient K1 by the number of pixels in the region of interest Ax1 and the value obtained by multiplying the second correction coefficient K2 by the number of pixels in the two adjacent regions Ax2 is zero.

[0099] The feature image generating unit 8c derives the first correction value corresponding to each pixel of the attention area Ax1 by multiplying each pixel value of the attention area Ax1 by a first correction coefficient K1, and derives the second correction value corresponding to each pixel of the two adjacent areas Ax2 by multiplying each pixel value of the two adjacent areas Ax2 by a second correction coefficient K2. Then, the feature image generating unit 8c derives a value obtained by integrating the first correction value and the second correction value as the conversion value of the pixel value of the attention pixel Px1.

[0100] Furthermore, the characteristic image generation unit 8c derives the conversion value by adding the sum or average value of multiple first correction values ​​corresponding to multiple pixels in the attention area Ax1 and the sum or average value of multiple second correction values ​​corresponding to multiple pixels in two adjacent areas Ax2.

[0101] The absolute value of the conversion value is a value obtained by amplifying the absolute value of the difference between the pixel values ​​of the attention area Ax1 and the pixel values ​​of the two adjacent areas Ax2. The process of deriving the conversion value by integrating the first correction value and the second correction value is an example of a process of emphasizing the difference between the pixel values ​​of the attention area Ax1 and the pixel values ​​of the two adjacent areas Ax2.

[0102] It is also possible that the first correction coefficient K1 is a negative number and the second correction coefficient K2 is a positive number.

[0103] For example, it is conceivable that the feature image generating unit 8c generates, as the first preprocessed image g11, first main map data including a plurality of the converted values ​​obtained by the main filtering process in which the main scanning direction D1 is set as the processing direction Dx1.

[0104] As shown in Figure 5, when test image g1 includes one or both of vertical streaks Ps11 and noise points Ps13, the first main map data in which one or both of the vertical streaks Ps11 and noise points Ps13 included in test image g1 are extracted is generated by the main filter processing in which the main scanning direction D1 is the processing direction Dx1.

[0105] Furthermore, when the test image g1 includes horizontal streaks Ps12, the first main map data from which the horizontal streaks Ps12 included in the test image g1 have been removed is generated by the main filtering process in which the main scanning direction D1 is set as the processing direction Dx1.

[0106] The vertical streak Ps11 corresponds to the first unique portion, the horizontal streak Ps12 corresponds to the second unique portion, and the noise point Ps13 corresponds to the third unique portion.

[0107] On the other hand, the second pre-processing includes the main filtering process in which the sub-scanning direction D2 is set as the processing direction Dx1.

[0108] For example, it is conceivable that the feature image generating unit 8c generates, as the second pre-processed image g12, second main map data including a plurality of the integrated values ​​obtained by the main filtering process with the sub-scanning direction D2 as the processing direction Dx1.

[0109] As shown in Figure 5, when test image g1 contains one or both of horizontal streaks Ps12 and noise points Ps13, the second main map data in which one or both of the horizontal streaks Ps12 and noise points Ps13 contained in test image g1 are extracted is generated by the main filter processing in which the sub-scanning direction D2 is the processing direction Dx1.

[0110] Furthermore, when the test image g1 includes vertical streaks Ps11, the second main map data from which the vertical streaks Ps11 included in the test image g1 have been removed is generated by the main filtering process in which the sub-scanning direction D2 is set as the processing direction Dx1.

[0111] However, in the main filter process, an erroneous integrated value having a positive and negative sign opposite to the integrated value expressing the original state of the singular part Ps1 may be derived at the edge parts at both ends of the singular part Ps1 in the processing direction Dx1. If such an erroneous integrated value is processed as a pixel value expressing the singular part Ps1, it may adversely affect the determination of the image defect.

[0112] Therefore, in this embodiment, the first pre-processing further includes an edge enhancement filtering process in which the main scanning direction D1 is the processing direction Dx1, in addition to the main filtering process in which the main scanning direction D1 is the processing direction Dx1.

[0113] Similarly, the second pre-processing further includes the edge emphasis filtering process in which the sub-scanning direction D2 is the processing direction Dx1, in addition to the main filtering process in which the sub-scanning direction D2 is the processing direction Dx1.

[0114] The edge enhancement filter process is a process for performing edge enhancement on the region of interest Ax1 and one of the two adjacent regions Ax2 that is determined in advance.

[0115] Specifically, the edge enhancement filter process converts the pixel value of a pixel of interest Px1, which is sequentially selected from the test image g1, into an edge intensity that combines a third correction value obtained by correcting the pixel value of the region of interest Ax1 with a positive or negative third correction coefficient K3, and a fourth correction value obtained by correcting the pixel value of one of the adjacent regions Ax2 with a fourth correction coefficient K4 that has an opposite positive and negative sign to the third correction coefficient K3 (see Figure 5).

[0116] 5, the third correction coefficient K3 is a positive coefficient, and the fourth correction coefficient K4 is a negative coefficient. The third correction coefficient K3 and the fourth correction coefficient K4 are set so that the sum of a value obtained by multiplying the third correction coefficient K3 by the number of pixels in the region of interest Ax1 and a value obtained by multiplying the fourth correction coefficient K4 by the number of pixels in one of the adjacent regions Ax2 is zero.

[0117] The edge emphasis filter process is performed with the main scanning direction D1 as the processing direction Dx1, thereby generating horizontal edge strength map data in which each pixel value of the test image g1 is converted into the edge strength.

[0118] Similarly, the edge emphasis filter process is performed with the sub-scanning direction D2 as the processing direction Dx1, whereby vertical edge strength map data is generated in which each pixel value of the test image g1 is converted into the edge strength.

[0119] In this embodiment, the characteristic image generating unit 8c generates the first main map data by the main filtering process with the main scanning direction D1 as the processing direction Dx1.

[0120] Furthermore, the characteristic image generating unit 8c generates the horizontal edge strength map data by executing the edge emphasis filter process with the main scanning direction D1 as the processing direction Dx1.

[0121] Furthermore, the characteristic image generating unit 8c generates a first preprocessed image g11 by correcting each pixel value of the first main map data with each corresponding pixel value of the horizontal edge strength map data. For example, the characteristic image generating unit 8c generates the first preprocessed image g11 by adding the absolute value of each pixel value of the horizontal edge strength map data to each pixel value of the first main map data.

[0122] Similarly, the characteristic image generating unit 8c generates the second main map data by executing the main filtering process with the sub-scanning direction D2 as the processing direction Dx1.

[0123] Furthermore, the characteristic image generating unit 8c generates the vertical edge strength map data by executing the edge emphasis filter process with the sub-scanning direction D2 as the processing direction Dx1.

[0124] Furthermore, the feature image generating unit 8c generates a second preprocessed image g12 by correcting each pixel value of the second main map data with each corresponding pixel value of the vertical edge strength map data. For example, the feature image generating unit 8c generates the second preprocessed image g12 by adding the absolute value of each pixel value of the vertical edge strength map data to each pixel value of the second main map data.

[0125] The singular portion extraction process is a process for generating three feature images g21, g22, and g23 in which vertical streaks Ps11, horizontal streaks Ps12, and noise points Ps13 contained in the first preprocessed image g11 or the second preprocessed image g12 are individually extracted. The three feature images g21, g22, and g23 are the first feature image g21, the second feature image g22, and the third feature image g23.

[0126] The first feature image g21 is an image obtained by extracting a singular portion Ps1 that exists in the first preprocessed image g11 and is not common to the first preprocessed image g11 and the second preprocessed image g12, out of the singular portion Ps1 consisting of one or more significant pixels in the first preprocessed image g11 and the second preprocessed image g12. The first feature image g21 does not include horizontal streaks Ps12 and noise points Ps13, but includes vertical streaks Ps11 if the first preprocessed image g11 includes the vertical streaks Ps11.

[0127] The significant pixels are pixels that can be distinguished from other pixels by comparing each pixel value or an index value based on each pixel value in the test image g1 with a predetermined threshold value.

[0128] The second feature image g22 is an image in which, of the singular portions Ps1 in the first preprocessed image g11 and the second preprocessed image g12, the singular portions Ps1 that exist in the second preprocessed image g12 and are not common to the first preprocessed image g11 and the second preprocessed image g12 are extracted. The second feature image g22 does not include vertical streaks Ps11 and noise points Ps13, but if the second preprocessed image g12 includes horizontal streaks Ps12, the second feature image g22 includes the horizontal streaks Ps12.

[0129] The third feature image g23 is an image in which the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 is extracted. The third feature image g23 does not include the vertical streaks Ps11 and the horizontal streaks Ps12, but includes the noise point Ps13 if the first preprocessed image g11 and the second preprocessed image g12 include the noise point Ps13.

[0130] There are various possible methods for generating the three feature images g21, g22, and g23 from the first preprocessed image g11 and the second preprocessed image g12.

[0131] For example, the feature image generating unit 8c derives the index value Zi by applying the first pixel value Xi, which is each pixel value in the first preprocessed image g11 that exceeds a predetermined reference value, and the second pixel value Yi, which is each pixel value in the second preprocessed image g12 that exceeds the reference value, to the following formula (1), where the subscript i is an identification number of the position of each pixel.

[0132]

number

[0133] The index value Zi of the pixels that make up the vertical stripes Ps11 is a relatively large positive number. The index value Zi of the pixels that make up the horizontal stripes Ps12 is an undetermined small negative number. The index value Zi of the pixels that make up the noise point Ps13 is 0 or a value close to 0. The index value Zi is an example of an index value of the difference between corresponding pixel values ​​in the first preprocessed image g11 and the second preprocessed image g12.

[0134] The above-mentioned properties of the index value Zi can be utilized to simplify the process of extracting vertical streaks Ps11 from the first preprocessed image g11, extracting horizontal streaks Ps12 from the second preprocessed image g12, and extracting noise points Ps13 from the first preprocessed image g11 or the second preprocessed image g12.

[0135] For example, the feature image generating unit 8c generates the first feature image g21 by converting the first pixel value Xi in the first preprocessed image g11 into a first uniqueness Pi derived by the following formula (2). As a result, the first feature image g21 in which the vertical streak Ps11 is extracted from the first preprocessed image g11 is generated.

[0136]

number

[0137] Furthermore, the feature image generating unit 8c generates a second feature image g22 by converting the second pixel value Yi in the second preprocessed image g12 into a second uniqueness Qi derived by the following formula (3). In this way, a second feature image g22 in which the horizontal streak Ps12 is extracted from the second preprocessed image g12 is generated.

[0138]

number

[0139] Furthermore, the feature image generating unit 8c generates a third feature image g23 by converting the first pixel value Xi in the first preprocessed image g11 into a third uniqueness Ri derived by the following formula (4). As a result, the third feature image g23 in which the noise point Ps13 is extracted from the first preprocessed image g11 is generated.

[0140]

number

[0141] Alternatively, the feature image generating unit 8c may generate the third feature image g23 by converting the second pixel value Yi in the second preprocessed image g12 into a third uniqueness Ri derived by the following formula (5). In this way, the third feature image g23 in which the noise point Ps13 is extracted from the second preprocessed image g12 is generated.

[0142]

number

[0143] As described above, the feature image generating unit 8c generates the first feature image g21 by converting each pixel value of the first preprocessed image g11 by the predetermined formula (2) based on the index value Zi. Formula (2) is an example of the first conversion formula.

[0144] Furthermore, the characteristic image generating unit 8c generates a second characteristic image g22 by converting each pixel value of the second preprocessed image g12 by a predetermined formula (3) based on the index value Zi. Formula (3) is an example of the second conversion formula.

[0145] Furthermore, the feature image generating unit 8c generates a third feature image g23 by converting each pixel value of the first preprocessed image g11 or the second preprocessed image g12 by a predetermined formula (4) or (5) based on the index value Zi. Formulas (4) and (5) are each an example of a third conversion formula.

[0146] The process of generating the first feature image g21, the second feature image g22, and the third feature image g23 in step S201 is an example of a process of extracting vertical streaks Ps11, horizontal streaks Ps12, and noise points Ps13 from one or more singular parts Ps1 in the first preprocessed image g11 and the second preprocessed image g12 as the image defects.

[0147] The process of generating the third feature image g23 in step S201 is an example of a process of generating an extracted image in which, among one or more singular parts Ps1 in the first preprocessed image g11 and the second preprocessed image g12, those common to the first preprocessed image g11 and the second preprocessed image g12 are extracted as noise points Ps13. The third feature image g23 is an example of the extracted image.

[0148] After the characteristic images g21, g22, and g23 are generated, the characteristic image generating unit 8c shifts the process to step S202.

[0149] <Process S202> In step S202, the singular portion identifying unit 8d identifies the position of the singular portion Ps1 in each of the feature images g21, g22, and g23. The processing in steps S201 and S201 is an example of a processing for identifying the singular portion Ps1 consisting of a plurality of significant pixels in the test image g1.

[0150] For example, the singular part identifying unit 8d determines that a portion having a pixel value outside a predetermined reference range in each of the feature images g21, g22, and g23 is the singular part Ps1. In step S202, a vertical streak Ps11 is identified from the first feature image g21, a horizontal streak Ps12 is identified from the second feature image g22, and a noise point Ps13 is identified from the third feature image g23.

[0151] Furthermore, the unique portion identifying unit 8d executes a combining process for each of the feature images g21, g22, and g23. The combining process is a process for combining a plurality of unique portions Ps1 into a single series of unique portions Ps1 when the plurality of unique portions Ps1 are present within a predetermined proximity range in each of the main scanning direction D1 and the sub-scanning direction D2.

[0152] For example, if the first feature image g21 includes two vertical streaks Ps11 spaced apart in the sub-scanning direction D2 within the proximity range, the singular part identification unit 8d combines the two vertical streaks Ps11 into a single vertical streak Ps11 by the combining process.

[0153] Similarly, when the second feature image g22 includes two horizontal streaks Ps12 spaced apart in the main scanning direction D1 within the proximity range, the unique part identification unit 8d combines the two horizontal streaks Ps12 into a single horizontal streak Ps12 by the combining process.

[0154] In addition, when the third feature image g23 includes multiple noise points Ps13 arranged at intervals in the main scanning direction D1 or the sub-scanning direction D2 within the proximity range, the singular part identification unit 8d combines the multiple noise points Ps13 into a single noise point Ps13 by the combining process.

[0155] After executing the process of step S202, the unique part identifying unit 8d shifts the process to step S203.

[0156] <Process S203> In step S203, the unique portion identifying unit 8d performs sheet noise removal processing on the third feature image g23.

[0157] The sheet noise removal process is a process for detecting point-like sheet noise contained in the test output sheet 9 itself from noise point Ps13 in the third feature image g23, and removing the sheet noise from the third feature image g23. The third feature image g23 that is the target of the sheet noise removal process is an image generated based on the first preprocessed image g11 and the second preprocessed image g12.

[0158] The sheet may have dot-like sheet noise formed during the manufacturing process. The sheet noise is not an image defect, and is therefore desirably removed from the third characteristic image g23.

[0159] For example, the unique part identifying unit 8d extracts a part including the noise point Ps13 in the third feature image g23 as a feature part, based on the position of the noise point Ps13 obtained in step S202.

[0160] Furthermore, the singular part identification unit 8d detects the sheet noise by performing a sheet noise pattern recognition process in which the characteristic part is used as an input image and, through pattern recognition of the input image, determines whether the input image is an image corresponding to the sheet noise.

[0161] Furthermore, the singular part identifying unit 8d removes the detected sheet noise from the third feature image g23. Furthermore, the singular part identifying unit 8d excludes the position of the detected sheet noise from the position of the noise point Ps13 identified for the third feature image g23 in step S202. That is, the singular part identifying unit 8d considers the sheet noise in the third feature image g23 to be not the singular part Ps1.

[0162] For example, the singular part identification unit 8d generates a third feature image g23 from which the sheet noise has been removed by converting the pixel value of the part of the noise point Ps13 in the third feature image g23 that has been determined to be the sheet noise into an interpolated value based on the surrounding pixel values.

[0163] For example, the sheet noise pattern recognition process is a process for determining whether or not the input image is an image corresponding to the sheet noise by a learning model that has been trained in advance using a plurality of sample images corresponding to the sheet noise as teacher data. The sheet noise pattern recognition process is the same process as the feature pattern recognition process described later.

[0164] The sample image corresponding to the sheet noise pattern recognition processing is the characteristic portion including the sheet noise in a test image g1 corresponding to a test output sheet 9 on which an original test image g01 not including the image defect is formed on the sheet including the sheet noise.

[0165] In addition, the learning model corresponding to the sheet noise pattern recognition process may be prepared for each type of sheet. In this case, the main control unit 8a selects the type of sheet corresponding to the test output sheet 9 from a plurality of candidates set in advance according to an operation on the operation unit 801.

[0166] Then, the singular portion specifying unit 8d executes the sheet noise pattern recognition process based on the learning model corresponding to the selected type of sheet.

[0167] For example, the learning model may be a model that employs a classification-type machine learning algorithm called Random Forest, a model that employs a machine learning algorithm called Support Vector Machine (SVM), or a model that employs a Convolutional Neural Network (CNN) algorithm.

[0168] Then, when the position of the singular part Ps1 is not identified in any of the three feature images g21, g22, and g23 by the processes of steps S202 and S203, the singular part identifying unit 8d ends the singular defect determination process. On the other hand, when the position of the singular part Ps1 is identified in one or more of the three feature images g21, g22, and g23, the singular part identifying unit 8d shifts the process to step S204.

[0169] The third feature image g23 that is the target of processing in steps S204 to S208 described below is the third feature image g23 from which the sheet noise has been removed.

[0170] <Process S204> In step S204, the color vector specifying unit 8e specifies a color vector that represents a vector in color space from one of the color of the singular portion Ps1 in the test image g1 and the color of the reference area including the periphery of the singular portion Ps1 to the other.

[0171] The reference region is a region of a predetermined range determined based on the unique portion Ps1. For example, the reference region is a region that includes a peripheral region adjacent to the unique portion Ps1 and does not include the unique portion Ps1. The reference region may also include the unique portion Ps1 and a peripheral region adjacent to the unique portion Ps1.

[0172] The test image g1 is originally a uniform halftone image. Therefore, when a good test image g1 is formed on the test output sheet 9, the singular portion Ps1 is not identified, and the color vectors at any positions in the test image g1 are generally zero vectors.

[0173] On the other hand, when a unique portion Ps1 is identified, the direction of the color vector between the unique portion Ps1 and the reference area corresponding to the unique portion Ps1 represents an excess or deficiency of toner concentration of any of the four developing colors in the image forming device 2.

[0174] Therefore, the direction of the color vector indicates which of the four image forming units 4x in the image forming apparatus 2 caused the occurrence of the singular portion Ps1.

[0175] The color vector specifying unit 8e may specify, as the color vector, a vector in a color space from one of the color of the singular part Ps1 in the test image g1 and a predetermined reference color to the other. In this case, the reference color is the original color of the test image g1.

[0176] Furthermore, in step S204, the color vector specifying unit 8e determines, based on the color vector, the developed color causing the singular portion Ps1 and the excess or deficiency state of the density of that developed color.

[0177] For example, information on a plurality of unit vectors representing the direction in which the density of each of cyan, magenta, yellow, or black increases and the direction in which the density is insufficient with respect to the reference color of test image g1 is stored in advance in secondary storage device 82.

[0178] The color vector specifying unit 8e normalizes the color vector to a predetermined unit length. Furthermore, the color vector specifying unit 8e determines which of the multiple unit vectors the normalized color vector is most similar to. In this way, the color vector specifying unit 8e determines the developing color causing the singular portion Ps1 and the excess or deficiency of the density of the developing color.

[0179] After executing the process of step S204, the color vector specification unit 8e shifts the process to step S205.

[0180] <Process S205> In step S205, if a singular portion Ps1 is identified in one or both of the second feature image g22 and the third feature image g23, the periodicity determination unit 8f transitions the process to step S206; otherwise, the periodicity determination unit 8f transitions the process to step S207.

[0181] In the following description, one or both of the second feature image g22 and the third feature image g23 in which the peculiar portion Ps1 is identified will be referred to as a periodicity determination target image. The peculiar portion Ps1 in the periodicity determination target image is a horizontal streak Ps12 or a noise point Ps13 (see FIG. 7).

[0182] <Process S206> In step S206, the periodicity determination unit 8f executes a periodic singular part determination process for the periodicity determination target image. The periodic singular part determination process includes a number determination process, a singular part periodicity determination process, and a singular part periodicity cause determination process.

[0183] The number determination process is a process for determining the number of unique portions Ps1 arranged in the sub-scanning direction D2 in the periodicity determination target image.

[0184] Specifically, the periodicity determination unit 8f determines the number of horizontal stripes Ps12 lined up in the sub-scanning direction D2 in the second feature image g22 by counting the number of horizontal stripes Ps12 lined up in the sub-scanning direction D2 whose portion occupying the same range in the main scanning direction D1 exceeds a predetermined ratio.

[0185] Furthermore, the periodicity determination unit 8f determines the number of noise points Ps13 lined up in the sub-scanning direction D2 by counting the number of noise points Ps13 whose positional deviation in the main scanning direction D1 is within a predetermined range and lined up in the sub-scanning direction D2 in the third feature image g23.

[0186] The periodicity determining unit 8f executes the singular portion periodicity determining process only for the singular portions Ps1 of which the number aligned in the sub-scanning direction D2 is two or more.

[0187] Furthermore, the periodicity determining section 8f determines that the singular portion Ps1 having one line in the sub-scanning direction D2 does not have periodicity, and skips the singular portion periodicity determining process and the singular portion periodicity cause determining process.

[0188] The singular part periodicity determination process is a process for determining the presence or absence of one or more predetermined periodicities in the sub-scanning direction D2 for the periodicity determination target image.

[0189] The periodicity corresponds to the outer circumferential length of a rotating body related to image formation, such as the photoconductor 41, charging roller 42a, developing roller 43a, or primary transfer roller 441 in each of the image forming units 4x or in the transfer device 44. The state of the rotating body related to image formation affects the quality of the image formed on the sheet. In the following description, the rotating body related to image formation is referred to as an image forming rotating body.

[0190] When the image defect occurs due to a defect in the imaging rotor, the periodicity corresponding to the outer circumferential length of the imaging rotor may appear as a spacing in the sub-scanning direction D2 between a plurality of horizontal streaks Ps12 or a plurality of noise points Ps13.

[0191] Therefore, when the image for which periodicity is to be determined has a periodicity corresponding to the outer circumferential length of the imaging rotor, it can be said that the imaging rotor corresponding to that periodicity is the cause of the horizontal streaks Ps12 or noise points Ps13 in the image for which periodicity is to be determined.

[0192] When the number of unique portions Ps1 aligned in the sub-scanning direction D2 in the periodicity determination target image is two, the periodicity determination unit 8f executes an interval derivation process as the unique portion periodicity determination process.

[0193] In the interval derivation process, the periodicity determination unit 8f derives the interval between the two unique portions Ps1 in the sub-scanning direction D2 as the period of the two unique portions Ps1.

[0194] When the number of the singular portions Ps1 aligned in the sub-scanning direction D2 in the periodicity determination target image is three or more, the periodicity determination section 8f executes a frequency analysis process as the singular portion periodicity determination process.

[0195] In the frequency analysis process, the periodicity determination unit 8f identifies a singular part frequency for the periodicity determination target image including three or more singular parts Ps1 aligned in the sub-scanning direction D2. The singular part frequency is a dominant frequency in the frequency distribution of the data string of the singular part Ps1 in the periodicity determination target image. The periodicity determination unit 8f identifies the singular part frequency by performing frequency analysis such as a Fourier transform.

[0196] Furthermore, the periodicity determining unit 8f derives the period corresponding to the singular part frequency as the periods of three or more singular parts Ps1.

[0197] In the singular part periodicity cause determination process, the periodicity determination unit 8f determines whether or not the outer circumferential length of each of the predetermined candidates for the imaging rotor satisfies a predetermined period approximation condition with respect to the period of the singular part Ps1 for each of the predetermined candidates for the imaging rotor in step S206. The plurality of candidates for the imaging rotor in step S206 are an example of the predetermined plurality of cause candidates corresponding to the horizontal streaks Ps12 or the noise points Ps13.

[0198] In the following description, the singular portion Ps1 of the target that is determined to satisfy the periodic approximation condition for any of the imaging rotor candidates is referred to as a periodic singular portion, and the other singular portions Ps1 are referred to as non-periodic singular portions. The periodic singular portion and the non-periodic singular portion are the singular portions Ps1 included in the second feature image g22 or the third feature image g23.

[0199] The periodicity determination unit 8f determines that one of the imaging rotor candidates determined to satisfy the periodic approximation condition in the singular part periodicity cause determination process is the cause of the periodic singular part, thereby determining the cause of the horizontal streak Ps12 or the noise point Ps13.

[0200] In addition, in step S206, the periodicity determination unit 8f determines, based on the color vector determined in step S204, which of the four image forming units 4x, each having a different development color, is the cause of the horizontal streak Ps12 or the noise point Ps13.

[0201] In addition, when three or more singular parts Ps1 arranged in the sub-scanning direction D2 include a non-periodic singular part that does not correspond to the singular part frequency, the periodicity determination unit 8f targets the non-periodic singular part for the feature pattern recognition processing described below.

[0202] For example, the periodicity determining unit 8f generates inverse Fourier transformed data by performing an inverse Fourier transform on the frequency distribution obtained by the Fourier transform, from which frequency components other than the singular part frequency have been removed.

[0203] Furthermore, the periodicity determination unit 8f determines, among the three or more unique parts Ps1 arranged in the sub-scanning direction D2, the one that is located at a position away from the peak position in the waveform in the sub-scanning direction D2 represented by the inverse Fourier transform data, as the non-periodic unique part.

[0204] Then, when it is determined in step S206 that the second feature image g22 and the third feature image g23 do not include the non-periodic singular portion, the periodicity determining unit 8f terminates the singular defect determination process.

[0205] On the other hand, if it is determined in step S206 that the second feature image g22 and the third feature image g23 include the non-periodic singular portion, the process proceeds to step S207.

[0206] <Process S207> In step S207, the noise point determination unit 8h performs a noise point determination process, which will be described later, on the third feature image g23 including the non-periodic singular part. The noise point determination process is a process for determining the cause of the noise point Ps13 that does not have periodicity in the third feature image g23.

[0207] After the processing of step S207, if the second feature image g22 or the third feature image g23 contains the undetermined non-periodic singular part, the noise point judgment unit 8h transitions the processing to step S208, and if not, terminates the singular defect judgment processing.

[0208] <Process S208> In step S208, the pattern recognition unit 8g performs a feature pattern recognition process on each of the first feature image g21, and the second and third feature images g22 and g23 each including the undetermined non-periodic singular portion.

[0209] In the feature pattern recognition process, a first feature image g21, and a second feature image g22 and a third feature image g23 including the undetermined non-periodic singular portion are set as input images. In the feature pattern recognition process, a pattern recognition unit 8g determines whether the input image corresponds to any one of a plurality of predetermined candidate causes corresponding to the image defect by pattern recognition of the input image.

[0210] The input image of the feature pattern recognition process may include the horizontal edge strength map data or the vertical edge strength map data obtained by the edge emphasis filter process. For example, in the feature pattern recognition process for determining a vertical streak Ps11, a first feature image g21 and the horizontal edge strength map data are used as the input image.

[0211] Similarly, in the feature pattern recognition process for determining the horizontal streak Ps12, the second feature image g22 and the vertical edge strength map data are used as the input image.

[0212] Similarly, in the feature pattern recognition process for determining the noise point Ps13, the third feature image g23 and either or both of the horizontal edge strength map data and the vertical edge strength map data are used as the input images.

[0213] For example, the feature pattern recognition process is a process of classifying the input image into one of the multiple candidate causes using a learning model that has been trained in advance using multiple sample images corresponding to the multiple candidate causes as training data.

[0214] For example, the learning model may be a model employing the random forest, a model employing the SVM, or a model employing the CNN algorithm.

[0215] The learning models are prepared individually for the first feature image g21, and the second and third feature images g22 and g23 each including the non-periodic singular portion. In addition, the sample images are used as the teacher data for each of the cause candidates.

[0216] Furthermore, in step S208, the pattern recognition unit 8g determines, based on the color vector determined in step S204, which of the four image forming units 4x, each with a different developing color, is the cause of the vertical streak Ps11, the horizontal streak Ps12, or the noise point Ps13.

[0217] By the process of step S208, the cause of the vertical streak Ps11 and the causes of the horizontal streak Ps12 and noise point Ps13 determined as the non-periodic singular portion are determined. After executing the process of step S207, the pattern recognition unit 8g ends the singular defect determination process.

[0218] [Noise point detection process] Next, an example of the procedure of the noise point determination process in step S207 will be described with reference to the flowchart shown in Fig. 5. In the following description, S301, S302, ... represent identification codes of multiple steps in the noise point determination process. The noise point determination process starts from step S301.

[0219] As described above, the noise point Ps13 to be subjected to the noise point determination process is the noise point Ps13 determined to be the non-periodic singular part. Also, the sheet noise is excluded from the noise point Ps13 to be subjected to the noise point determination process by the sheet noise removal process.

[0220] <Process S301> In step S301, the noise point determination unit 8h derives a plurality of feature parameters for each noise point Ps13 in the third feature image g23. In this embodiment, the plurality of feature parameters include the edge strength of the noise point Ps13, the degree of flattening of the noise point Ps13, and the number of edges based on the pixel value distribution in a crossing pixel row AR1 that is a pixel row that crosses the noise point Ps13 (see FIG. 9).

[0221] 9, each pixel value V1 in the transverse pixel row AR1 is a value representing the luminance of each pixel. However, each pixel value V1 in the transverse pixel row AR1 may be a value representing the density of each pixel.

[0222] The edge strength represents the degree of difference between a pixel value V1 of the noise point Ps13 in the third feature image g23 and a pixel value V1 of the adjacent region AN1 adjacent to the noise point Ps13. For example, the noise point determination unit 8h derives the difference between a representative value of the pixel values ​​V1 of the noise point Ps13 and a representative value of the pixel values ​​V1 of the adjacent region AN1 as the edge strength.

[0223] Furthermore, the noise point determination unit 8h may derive, as the edge intensity, a pixel value V1 of the outer edge portion of the noise point Ps13 in an image obtained by performing a known edge enhancement process on the third feature image g23.

[0224] The noise point determination unit 8h also derives the ratio of the length of the noise point Ps13 in the main scanning direction D1 to the length in the sub-scanning direction D2, the circularity or flattening ratio, etc. as the degree of flattening. The circularity means the degree of deviation of a circular shape from a geometrically correct circle.

[0225] Furthermore, the noise point determination unit 8h may identify the longitudinal direction DL1 of the noise point Ps13, and derive the ratio of the length of the noise point Ps13 in the longitudinal direction DL1 to the length in the short direction perpendicular to the longitudinal direction DL1 as the degree of flatness.

[0226] The noise point determination unit 8h also sets a transverse line L1 that crosses the noise point Ps13, and identifies pixel rows along the transverse line L1 in the noise point Ps13 and in the adjacent area AN1 on both sides of the noise point Ps13 as transverse pixel rows AR1.

[0227] For example, the noise point determination unit 8h sets a line that passes through the center of the noise point Ps13 in the short side direction and extends along the longitudinal direction DL1 as the transverse line L1.

[0228] Furthermore, the noise point determining unit 8h derives, as the number of edges, the number of points in the cross-sectional pixel row AR1 where the change in pixel value V1 exceeds the allowable range.

[0229] For example, the noise point determining unit 8h derives, as the number of edges, the number of points where the differential value dV1 of the pixel value V1 in the cross-sectional pixel row AR1 falls outside a predetermined first reference range RA1 (see FIG. 9).

[0230] Furthermore, the noise point determining unit 8h may derive the number of points where the pixel value V1 in the cross-sectional pixel row AR1 crosses the reference value VA1 or the second reference range RA2 as the number of edges (see FIG. 9).

[0231] For example, the noise point determination unit 8h sets the reference value VA1 or the second reference range RA2 based on the pixel value V1 of the adjacent region AN1. The pixel value V1 of the adjacent region AN1 is an example of the pixel value V1 of the cross-sectional pixel row AR1.

[0232] After deriving the characteristic parameters, the noise point determining unit 8h shifts the process to step S302.

[0233] <Process S302> In step S302, the noise point determination unit 8h executes a first cause determination process using the three feature parameters. The first cause determination process is a process for determining whether or not the cause of the noise point Ps13 in the third feature image g23 is the abnormal development.

[0234] For example, the noise point determining unit 8h derives a first evaluation value according to the number of edges, a second evaluation value according to the edge strength, and a third evaluation value according to the degree of flattening.

[0235] 9A to 9C show an example of the noise point Ps13 when the cause is the abnormal development. Fig. 9A shows a case where the symptom of the noise point Ps13 is mild, Fig. 9B shows a case where the symptom of the noise point Ps13 is moderate, and Fig. 9C shows a case where the symptom of the noise point Ps13 is severe.

[0236] As shown in Fig. 9, the more severe the noise point Ps13 caused by the abnormal development becomes, the more of a ring-shaped image it becomes, and the more the ring-shaped images are linked together along a straight line. This phenomenon does not occur in the noise point Ps13 caused by factors other than the abnormal development. Therefore, the greater the number of edges, the more likely it is that the noise point Ps13 is caused by the abnormal development.

[0237] Therefore, the noise point determining section 8h sets the first evaluation value which indicates that the greater the number of edges, the greater the possibility that the cause of the noise point Ps13 is the abnormal development.

[0238] 9, the more severe the noise point Ps13 caused by the abnormal development becomes, the flatter it becomes. This phenomenon does not occur in the noise point Ps13 caused by factors other than the abnormal development. Therefore, the flatter the degree of flatness, the more likely it is that the noise point Ps13 is caused by the abnormal development.

[0239] Therefore, the noise point determining section 8h sets the second evaluation value which indicates that the noise point Ps13 is more likely to be caused by the abnormal development as the degree of flatness becomes flatter.

[0240] Further, the noise point Ps13 caused by the abnormal development has a larger edge strength than the noise point Ps13 caused by other factors. Therefore, the noise point determination unit 8h sets the third evaluation value, which indicates that the noise point Ps13 is more likely to be caused by the abnormal development as the edge strength increases.

[0241] For example, the noise point judgment unit 8h judges that the cause of the noise point Ps13 is the abnormal development if the integrated evaluation value obtained by integrating the first evaluation value, the second evaluation value, and the third evaluation value exceeds a predetermined threshold value, and judges that the cause of the noise point Ps13 is not the abnormal development if this is not the case.

[0242] For example, the integrated evaluation value is a total value or a weighted average value of the first evaluation value, the second evaluation value, and the third evaluation value.

[0243] In addition, the noise point judgment unit 8h may judge that the cause of the noise point Ps13 is the abnormal development if at least one of the first evaluation value, the second evaluation value, and the third evaluation value exceeds the corresponding threshold value, and may judge that the cause of the noise point Ps13 is not the abnormal development if this is not the case.

[0244] In the following description, the noise points Ps13 in the third feature image g23 that are determined to be caused by the abnormal development are referred to as developed color points.

[0245] Furthermore, the noise point determining section 8h determines in which of the four image forming sections 4x the abnormal development has occurred, in accordance with the color vector corresponding to the developed color point.

[0246] After executing the first cause determination process, the noise point determination unit 8h shifts the process to step S303. The process of step S302 is an example of a process for determining whether or not the cause of the noise point Ps13 is the abnormal development by using the first evaluation value, the second evaluation value, and the third evaluation value.

[0247] As described above, in steps S301 and S302, the noise point determination unit 8h determines whether or not the cause of the noise point Ps13 is the abnormal development by using at least one of the edge strength of the noise point Ps13, the degree of flattening of the noise point Ps13, and the pixel value distribution in the transverse pixel row AR1 in the third feature image g23.

[0248] <Process S303> In step S303, the noise point determination unit 8h excludes the developed color point among the noise points Ps13 from the targets of the subsequent processes of steps S304 to S306. After that, the noise point determination unit 8h shifts the process to step S304.

[0249] <Process S304> In step S304, the noise point determination unit 8h determines whether the color of the noise point Ps13 in the test image g1 is chromatic or achromatic.

[0250] Usually, when the noise point Ps13 occurs due to the waste toner falling or the carrier development, the color of the noise point Ps13 is an achromatic color such as black.

[0251] Therefore, when the noise point determination unit 8h determines that the color of the noise point Ps13 is colored, it terminates the noise point determination process without determining whether the cause of the noise point Ps13 is the waste toner falling or the carrier development.

[0252] On the other hand, if the noise point determining unit 8h determines that the color of the noise point Ps13 is achromatic, it shifts the process to step S305.

[0253] <Process S305> In step S305, the noise point determination unit 8h identifies an original drawing portion Pd1 that is a portion in the test image g1 that should have been originally drawn (see FIGS. 10 and 11).

[0254] When the test image g1 is an image that expresses gradation by a screen made up of a plurality of line images, the noise point determination unit 8h identifies the screen portion as the original drawing portion Pd1. FIG. 10 is an enlarged view of a part of the test output sheet 9.

[0255] In the example shown in Fig. 10, the test image g1 is an image that expresses shading by a line screen consisting of a plurality of line images each extending in a direction intersecting the sub-scanning direction D2. In this case, the noise point determination unit 8h identifies the line screen portion as the original drawing portion Pd1. Note that the sub-scanning direction D2 is the vertical direction in the test image g1.

[0256] Here, the coordinates representing the position of a part of the line screen or the outline of the line screen specified by a predetermined formula are referred to as logical coordinates. Also, the coordinates representing the position of a part of the test image g1 corresponding to the logical coordinates or the outline of the test image g1 are referred to as target coordinates.

[0257] For example, the noise point determination unit 8h derives correction coefficients for the direction and size so that the logical coordinates are most similar to the target coordinates. Furthermore, the noise point determination unit 8h corrects the position of the line screen specified by the logical formula with the correction coefficients, thereby specifying the position of the original drawing portion Pd1.

[0258] It is also possible that the original test image g01 and the test image g1 each include a plurality of reference marks of a predetermined shape. In this case, the noise point determination unit 8h identifies the positions of the plurality of reference marks in the test image g1, and identifies the original drawing portion Pd1 based on the positions of the plurality of reference marks. Note that the portion of the reference marks in the test image g1 is excluded from the target of the image defect determination.

[0259] After executing the process of step S305, the noise point determining unit 8h shifts the process to step S306.

[0260] <Process S306> In step S306, the noise point determination unit 8h executes a second cause determination process. The second cause determination process is a process for determining which of two predetermined cause candidates is the cause of the noise point Ps13 by determining the degree of overlap between the original drawing portion Pd1 and the noise point Ps13. The two cause candidates are the waste toner drop and the carrier development.

[0261] In FIG. 11, a first noise point Ps131 represents the noise point Ps13 caused by the falling waste toner, and a second noise point Ps132 represents the noise point Ps13 caused by the carrier development.

[0262] The first noise point Ps131 can occur anywhere regardless of the position of the original drawing portion Pd1. Therefore, the first noise point Ps131 often overlaps with the original drawing portion Pd1. On the other hand, the second noise point Ps132 is likely to occur in a dark potential portion on the surface of the photoconductor 41, and therefore often does not overlap with the original drawing portion Pd1.

[0263] Therefore, the noise point determination unit 8h derives an overlap rate, which is the ratio of the noise points Ps13 in the test image g1 that actually overlap with the drawing portion Pd1. If the overlap rate is lower than a predetermined rate, the noise point determination unit 8h determines that the noise point Ps13 is caused by the carrier development, and if not, determines that the noise point Ps13 is caused by the waste toner fall.

[0264] As described above, the noise point determining unit 8h determines whether the cause of the noise point Ps13 determined to be an achromatic color is the waste toner falling or the carrier development in steps S305 and S306.

[0265] However, for the above judgment, the gap of the imaged portion Pd1 must be larger than the diameter of the second noise point Ps132 caused by the carrier development. Therefore, it is desirable that the test image g1 is formed by a screen consisting of a plurality of line images arranged at intervals of at least 40 micrometers.

[0266] In addition, the first noise point Ps131 caused by the waste toner fall may be formed in a vertically elongated shape. Therefore, it is preferable that the test image g1 is an image that expresses shading by the line screen composed of a plurality of line images each extending in a direction intersecting with the sub-scanning direction D2. This makes it easy for the vertically elongated first noise point Ps131 to overlap with the original drawing portion Pd1.

[0267] In the example shown in FIG. 10, the test image g1 is an image that expresses shading by the line screen that is made up of a plurality of line images each extending in a direction that obliquely intersects with the sub-scanning direction D2.

[0268] After the process of step S306, the noise point determination unit 8h ends the noise point determination process.

[0269] [Voltage correction processing] Next, with reference to the flowchart shown in FIG. 6, an example of the procedure of the voltage correction process that is executed when it is determined that the cause of the noise point Ps13 is the abnormal development will be described.

[0270] In the voltage correction process, the main control unit 8a corrects the gap voltage by correcting one or both of the charging voltage output by the charging device 42 to the photoconductor 41 and the developing bias output by the bias output circuit 430 to the developing roller 43a.

[0271] The gap voltage is the maximum potential difference between the dark potential of the surface of the photoconductor 41 and the developing bias. The dark potential is the potential of the portion of the surface of the photoconductor 41 that has been charged by the charging device 42 before the electrostatic latent image is written.

[0272] Since excessive correction of the charging voltage, the developing bias, and the gap voltage can cause image defects other than the abnormal development, the gap voltage is corrected within a predetermined allowable correction range.

[0273] In the following description, S401, S402, ... represent identification symbols of a plurality of steps in the voltage correction process. The voltage correction process starts from step S401.

[0274] <Process S401> In step S401, if the current state of the gap correction still has a margin relative to the allowable correction range, the main control unit 8a shifts the process to step S402, and if not, shifts the process to step S404.

[0275] <Process S402> In step S402, the main control unit 8a outputs a correction command to one or both of the charging device 42 and the bias output circuit 430 to correct the gap voltage to a smaller value.

[0276] The correction command includes one or both of a command to the charging device 42 to decrease the charging voltage and a command to the bias output circuit 430 to increase the developing bias.

[0277] For example, the main control unit 8a sets the correction amount of the gap voltage according to the degree of defect of the noise point Ps13. The degree of defect of the noise point Ps13 is, for example, the number of noise points Ps13 in the third feature image g23 identified in step S202 of Fig. 4. In this case, the main control unit 8a sets the correction command so that the gap voltage is smaller when the number of noise points Ps13 is large than when the number of noise points Ps13 is small.

[0278] Furthermore, the main control unit 8a may use the number of edges of the noise point Ps13 derived in step S301 of FIG. 5 as the degree of defect of the noise point Ps13.

[0279] After the process of step S402, the main control unit 8a advances the process to step S403.

[0280] <Process S403> In step S403, the main control unit 8a records the degree of defect of the noise point Ps13 when it is determined that the cause of the noise point Ps13 is the abnormal development, and history information related to the correction command according to the determination of the abnormal development, in the secondary storage device 82. The secondary storage device 82 is an example of a non-volatile storage device.

[0281] After the voltage correction process is performed, there may be a case where the cause of the noise point Ps13 is determined to be the abnormal development. In this case, the main control unit 8a sets the content of the correction command based on the history information in step S402 of the new voltage correction process.

[0282] For example, the main control unit 8a sets the current correction amount of the gap voltage depending on the correction amount of the gap voltage in the history information and the degree of improvement from the degree of defect of the previous noise point Ps13 in the history information to the degree of defect of the current noise point Ps13.

[0283] After the process of step S403, the main control unit 8a ends the voltage correction process.

[0284] <Process S404> In step S404, the main control unit 8a outputs a predetermined warning notification. For example, the warning notification is output by causing the display unit 802 to display a message indicating that the noise point Ps13 cannot be eliminated by the voltage correction process.

[0285] After the process of step S404, the main control unit 8a ends the voltage correction process.

[0286] In this embodiment, the main control unit 8a executes an improvement notification process when the noise point Ps13 in a new test image g1 is identified by the singular part identification unit 8d after the voltage correction process is executed. The improvement notification process is a process of determining the improvement status of the noise point Ps13 based on the history information and notifying the result of the determination.

[0287] For example, in the improvement notification process, the main control unit 8a calculates an improvement rate from the number of noise points Ps13 in the previous history information to the number of noise points Ps13 in the current new test image g1. Furthermore, the main control unit 8a causes the display unit 802 to display a message indicating that an improvement in the noise points Ps13 equivalent to the improvement rate has been achieved by the improvement notification process.

[0288] Furthermore, the main control unit 8a may execute the processes of step S402 and step S403 in sequence for each of the four developing colors in the voltage correction process. In this case, when the specific defect determination process is executed for a new test image g1, the main control unit 8a calculates the improvement rate for each of the developing colors based on the history information.

[0289] Furthermore, the main control unit 8a maintains the gap voltage correction amount for the developing color corresponding to the improvement rate that satisfies a predetermined validity condition. For example, the validity condition is a condition that the improvement rate exceeds a predetermined value. Alternatively, the validity condition may be a condition that the improvement rate is the largest value among the improvement rates corresponding to four developing colors.

[0290] On the other hand, the main control unit 8a returns the gap voltage correction amount for the developing color corresponding to the improvement rate that does not satisfy the valid condition to the previous state. As a result, the gap voltage is appropriately corrected for each developing color. Note that information on the previous gap voltage correction amount is included in the history information.

[0291] As described above, the feature image generating unit 8c generates the third feature image g23 by extracting the noise point Ps13, which is one type of the image defect, from the test image g1 (see step S201 in FIG. 4).

[0292] Furthermore, the noise point determination unit 8h determines the cause of the noise point Ps13 using at least one of the edge strength of the noise point Ps13 in the third feature image g23, the degree of flattening of the noise point Ps13, and the pixel value distribution in the transverse pixel row AR1 that crosses the noise point Ps13 (see steps S301 and S302 in Figure 5).

[0293] In this embodiment, the noise point determination unit 8h derives the number of edges from the pixel value distribution in the cross-sectional pixel row AR1. Furthermore, the noise point determination unit 8h uses at least the number of edges to determine whether the noise point Ps13 is caused by the abnormal development.

[0294] According to this embodiment, the cause of the noise point Ps13, which is one type of image defect, can be correctly determined based on the test image g1 obtained by subjecting the test output sheet 9 to the image reading process.

[0295] The feature extraction process is a simple process with a small computational load. By using such a simple process, three feature images g21, g22, and g23, in which the unique portions Ps1 having different shapes are individually extracted from one test image g1, can be generated.

[0296] Then, the periodicity determination unit 8f executes the periodic singular part determination process for the singular part Ps1 in the second feature image g22 or the third feature image g23 (see step S206 in FIG. 4 and FIG. 7). As a result, the periodicity determination unit 8f determines the cause of the horizontal streaks Ps12 or the noise points Ps13 according to the result of the periodicity determination.

[0297] If horizontal streaks Ps12 or noise points Ps13 occur due to a defect in the rotating body related to image formation, the cause of the horizontal streaks Ps12 or noise points Ps13 can be determined with high accuracy by the periodic singular part determination process, which determines the periodicity corresponding to the outer circumferential length of the rotating body.

[0298] Furthermore, the noise point determination unit 8h executes the noise point determination process for the noise point Ps13 whose cause has not been determined by the periodic singular part determination process (see steps S206 to S207 in FIG. 4 and FIGS. 5, 7, and 8). As a result, if the cause of the noise point Ps13 is a cause other than poor rotation of the imaging rotator, the cause can be determined with high accuracy.

[0299] Furthermore, the pattern recognition unit 8g executes the feature pattern recognition process for the singular part Ps1 whose cause has not been determined by the periodic singular part determination process and the noise point determination process (see steps S206 to S208 in FIG. 4). This makes it possible to determine various causes of the singular part Ps1.

[0300] In addition, since the cause of the image defect is determined individually for the three feature images g21, g22, and g23, each of which includes a different type of singular portion Ps1, the cause of the image defect can be determined with high accuracy through a relatively simple determination process.

[0301] [First application example] Next, the procedure of the feature image generating process in the first application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0302] In the following description, S501, S502, ... represent identification codes of a plurality of steps in the feature image generating process according to this application example. The feature image generating process according to this application example starts from step S501.

[0303] <Process S501> In step S501, the characteristic image generating unit 8c selects a compression rate to be adopted from a plurality of preset compression rate candidates, and moves the process to step S502.

[0304] <Process S502> In step S502, the characteristic image generating unit 8c compresses the scanned image at the selected compression ratio to generate a test image g1. The processes in steps S501 and S502 are an example of a compression process. After that, the characteristic image generating unit 8c shifts the process to step S503.

[0305] <Process S503> In step S503, the feature image generating unit 8c generates a first preprocessed image g11 by performing the first preprocessing on the compressed test image g1 obtained in step S501. After that, the feature image generating unit 8c shifts the process to step S504.

[0306] <Process S504> In step S504, the feature image generating unit 8c generates a second preprocessed image g12 by performing the second preprocessing on the compressed test image g1 obtained in step S501. After that, the feature image generating unit 8c shifts the process to step S505.

[0307] <Process S505> In step S505, if the processes of steps S501 to S504 have been performed for all of the multiple compression ratio candidates, the characteristic image generation unit 8c transitions to step S506; if not, it performs the processes of steps S501 to S504 for different compression ratios.

[0308] In the compression processing in steps S501 and S502, the characteristic image generating unit 8c generates a plurality of test images g1 having different sizes by compressing the read image at a plurality of compression ratios.

[0309] Furthermore, in steps S503 and S504, the characteristic image generation unit 8c performs the first pre-processing and the second pre-processing on a plurality of test images g1 to generate a plurality of first pre-processed images g11 and a plurality of second pre-processed images g12, each of which corresponds to the plurality of test images g1.

[0310] <Process S506> In step S506, the feature image generating unit 8c executes the singular part extraction process on each of the first preprocessed images g11 and the second preprocessed images. As a result, the feature image generating unit 8c generates multiple candidates for the first feature image g21, the second feature image g22, and the third feature image g23 corresponding to the test images g1. After that, the feature image generating unit 8c shifts the process to step S507.

[0311] <Process S507> In step S507, the feature image generating unit 8c generates a first feature image g21, a second feature image g22, and a third feature image g23 by aggregating the candidates obtained in step S506. Thereafter, the feature image generating unit 8c ends the feature image generation process.

[0312] For example, the feature image generating unit 8c sets a representative value such as the maximum or average value of each pixel value in a plurality of candidates for the first feature image g21 as each pixel value of the first feature image g21. The same applies to the second feature image g22 and the third feature image g23.

[0313] The processes in steps S501 to S504 are an example of a process for generating a plurality of first preprocessed images g11 and a plurality of second preprocessed images g12 by performing the first preprocessing and the second preprocessing multiple times with different size ratios between the test image g1 and the attention area Ax1 and the adjacent area Ax2. Changing the compression rate is an example of changing the size ratio between the test image g1 and the attention area Ax1 and the adjacent area Ax2.

[0314] Furthermore, the processing of steps S506 to S507 is an example of a process for generating a first feature image g21, a second feature image g22, and a third feature image g23 by the singular part extraction process based on a plurality of first preprocessed images g11 and a plurality of second preprocessed images g12.

[0315] By employing this application example, vertical streaks Ps11 or horizontal streaks Ps12 of different thicknesses, or noise points Ps13 of different sizes can be extracted without omission.

[0316] [Second application example] Next, the procedure of the feature image generating process in the second application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0317] In the following description, S601, S602, ... represent identification codes of a plurality of steps in the feature image generation processing according to this application example. The feature image generation processing according to this application example starts from step S601.

[0318] <Process S601~S605> The characteristic image generating unit 8c executes the processes of steps S601 to S605, which are the same processes as steps S501 to S505. In step S605, when the processes of steps S601 to S604 have been executed for all of the multiple compression rate candidates, the characteristic image generating unit 8c shifts the process to step S606.

[0319] <Process S606> In step S606, the feature image generating unit 8c aggregates each of the plurality of first preprocessed images g11 and the plurality of second preprocessed images into one. After that, the feature image generating unit 8c shifts the process to step S607.

[0320] For example, the feature image generating unit 8c sets a representative value such as a maximum value or an average value of each pixel value in the multiple first preprocessed images g11 as each pixel value of the aggregated first feature image g21. The same applies to the multiple second preprocessed images g12.

[0321] <Process S606> In step S606, the feature image generating unit 8c performs the singular part extraction process on the aggregated first preprocessed image g11 and second preprocessed image g12 to generate a first feature image g21, a second feature image g22, and a third feature image g23. Thereafter, the feature image generating unit 8c ends the feature image generation process.

[0322] When this application example is adopted, the same effects as when the first application example is adopted can be obtained.

[0323] [Third application example] Next, the procedure of the feature image generating process in the third application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0324] In the following description, S701, S702, ... represent identification codes of a plurality of steps in the feature image generating process according to this application example. The feature image generating process according to this application example starts from step S701.

[0325] In the following description, the sizes of the region of interest Ax1 and the adjacent region Ax2 in the first pre-processing and the second pre-processing are referred to as filter sizes.

[0326] <Process S701> In step S701, the characteristic image generating unit 8c selects the filter size to be adopted from a plurality of preset size candidates, and moves the process to step S702.

[0327] <Process S702> In step S702, the feature image generating unit 8c generates a first preprocessed image g11 by performing the first preprocessing on the test image g1 with the filter size selected in step S701. After that, the feature image generating unit 8c shifts the process to step S703.

[0328] <Process S703> In step S703, the feature image generating unit 8c generates a second preprocessed image g12 by performing the second preprocessing on the test image g1 with the filter size selected in step S701. After that, the feature image generating unit 8c shifts the process to step S704.

[0329] <Process S704> In step S704, if the processes of steps S701 to S703 have been performed for all of the multiple size candidates, the characteristic image generation unit 8c transitions to step S705; if not, it performs the processes of steps S701 to S703 using different filter sizes.

[0330] In steps S701 to S704, the feature image generating unit 8c performs the first pre-processing multiple times and the second pre-processing multiple times on one test image g1 with different sizes of the attention area Ax1 and the adjacent area Ax2, thereby generating a plurality of first pre-processed images g11 and a plurality of second pre-processed images g12.

[0331] <Process S705, S706> In steps S705 and S706, the feature image generating unit 8c executes the same processes as steps S506 and S507 in Fig. 12. Thereafter, the feature image generating unit 8c ends the feature image generating process.

[0332] By the processing of steps S705 and S706, multiple candidates for the first feature image g21, the second feature image g22, and the third feature image g23 are aggregated, and the aggregated first feature image g21, the second feature image g22, and the third feature image g23 are generated.

[0333] The processes in steps S701 to S704 are an example of a process of generating a plurality of first preprocessed images g11 and a plurality of second preprocessed images g12 by performing the first preprocessing and the second preprocessing multiple times with different size ratios between the test image g1 and the attention area Ax1 and the adjacent area Ax2. Changing the filter size is an example of changing the size ratio between the test image g1 and the attention area Ax1 and the adjacent area Ax2.

[0334] By employing this application example, vertical streaks Ps11 or horizontal streaks Ps12 of different thicknesses, or noise points Ps13 of different sizes can be extracted without omission.

[0335] [Fourth application example] Next, the procedure of the feature image generating process in the fourth application example of the image processing device 10 will be described with reference to the flowchart shown in FIG.

[0336] In the following description, S801, S802, ... represent identification codes of a plurality of steps in the feature image generation processing according to this application example. The feature image generation processing according to this application example starts from step S801.

[0337] <Process S801~S804> The characteristic image generating unit 8c executes the processes of steps S801 to S804, which are the same processes as steps S701 to S704. In step S804, when the processes of steps S801 to S803 have been executed for all of the plurality of size candidates, the characteristic image generating unit 8c shifts the process to step S805.

[0338] <Process S805, S806> Furthermore, the characteristic image generating unit 8c executes the processes of steps S805 and S806, which are the same processes as steps S606 and S607. Thereafter, the characteristic image generating unit 8c ends the characteristic image generating process.

[0339] When this application example is adopted, the same effects as when the first application example is adopted can be obtained.

[0340] [Fifth application example] Next, the feature image generating process in the fifth application example of the image processing device 10 will be described.

[0341] In this application example, the feature image generation unit 8c distinguishes between pixels that constitute the singular portion Ps1 and pixels that do not by comparing each pixel value of the first preprocessed image g11 and the second preprocessed image g12 with a predetermined reference range.

[0342] That is, in this application example, the characteristic image generating unit 8c specifies the singular portion Ps1 based on the magnitude of each pixel value of the first preprocessed image g11 and the second preprocessed image g12 in the singular portion extraction process.

[0343] Furthermore, the feature image generating unit 8c extracts vertical streaks Ps11 from the singular portion Ps1 of the first preprocessed image g11 by removing the singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12.

[0344] Furthermore, the feature image generating unit 8c extracts horizontal streaks Ps12 from the unique portion Ps1 of the second preprocessed image g12 by removing the unique portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12.

[0345] Furthermore, the feature image generating unit 8c extracts a singular portion Ps1 common to the first preprocessed image g11 and the second preprocessed image g12 as a noise point Ps13.

[0346] For example, the characteristic image generating unit 8c generates a first characteristic image g21 by converting the first pixel value Xi determined to be other than the vertical streak Ps11 in the first preprocessed image g11 into an interpolated value based on the surrounding pixel values.

[0347] Similarly, the characteristic image generating unit 8c generates a second characteristic image g22 by converting the second pixel values ​​Yi determined to be other than the horizontal streak Ps12 in the second preprocessed image g12 into interpolated values ​​based on the surrounding pixel values.

[0348] Similarly, the feature image generating unit 8c generates a third feature image g23 by converting the first pixel value Xi determined to be other than the noise point Ps13 in the first preprocessed image g11 into an interpolated value based on the surrounding pixel values.

[0349] Alternatively, the feature image generating unit 8c may generate the third feature image g23 by converting the second pixel value Yi determined to be other than the noise point Ps13 in the second preprocessed image g12 into an interpolated value based on the surrounding pixel values.

[0350] [Sixth Application Example] The image defect determination process in the sixth application example of the image processing device 10 will be described below.

[0351] In general, it may be difficult for the image sensor 1a to accurately detect the shading of the yellow part in an image in which yellow and other colors are mixed, depending on the density of each color. Similarly, it may be difficult for the image sensor 1a to accurately detect the shading of the colored part in an image in which black and colored colors are mixed, depending on the density of each color.

[0352] In this application example, the job control section 8b causes two or three test output sheets 9, on which different types of original test images g01 are formed, to be output in the test print process.

[0353] When three test output sheets 9 are output, one sheet is formed with an original mixed color test image formed by combining a uniform cyan monochrome halftone image and a uniform magenta monochrome halftone image, another sheet is formed with an original yellow test image which is a uniform yellow monochrome halftone image, and another sheet is formed with an original gray test image which is a uniform black monochrome halftone image.

[0354] When two test output sheets 9 are output, one sheet has a mixed color test image formed by combining a uniform cyan monochrome halftone image, a uniform magenta monochrome halftone image, and a uniform yellow monochrome halftone image, and the other sheet has the original gray test image formed thereon.

[0355] Therefore, the test image g1 in this application example includes a mixed color test image, a yellow test image, and a gray test image corresponding to the original mixed color test image, the original yellow test image, and the original gray test image, respectively.

[0356] The yellow test image and the gray test image are halftone images of one developed color different from the color mixed into the mixed-color test image. The yellow test image and the gray test image are each an example of a single-color test image.

[0357] In this application example, the characteristic image generating unit 8c generates a first characteristic image g21, a second characteristic image g22, and a third characteristic image g23 for each of the mixed color test image and the single color test image read from the multiple test output sheets 9.

[0358] Furthermore, in this application example, the unique portion identifying unit 8d identifies the position of the unique portion Ps1 for the first characteristic image g21, the second characteristic image g22, and the third characteristic image g23 corresponding to the mixed color test image and the single color test image, respectively.

[0359] In this application example, the color vector specifying unit 8e, the periodicity determining unit 8f, the noise point determining unit 8h, and the pattern recognizing unit 8g execute a process for determining the cause of the image defect using the first feature image g21, the second feature image g22, and the third feature image g23 corresponding to the color mixing test image, as in the above embodiment.

[0360] In this application example, the periodicity determination unit 8f, the noise point determination unit 8h, and the pattern recognition unit 8g determine the cause of the singular portion Ps1 in the color mixing test image from among the multiple image forming units 4x corresponding to the multiple developed colors mixed in the color mixing test image, except for the case where the cause of the singular portion Ps1 is determined to be other than the image forming unit 4x.

[0361] Furthermore, in this application example, the periodicity judgment unit 8f, the noise point judgment unit 8h, and the pattern recognition unit 8g perform processing to determine the cause of the image defect using the first feature image g21, the second feature image g22, and the third feature image g23 corresponding to the monochrome test image.

[0362] In this application example, the periodicity determination unit 8f, the noise point determination unit 8h, and the pattern recognition unit 8g determine the cause of the image defect corresponding to the singular portion in the monochromatic test image from one of the multiple imaging units 4x that corresponds to the color of the monochromatic test image, except for the case where the cause of the singular portion Ps1 is determined to be other than the imaging unit 4x.

[0363] When this application example is adopted, it is also possible to determine the cause of the image defect based on the test output sheets 9 which are fewer in number than the total number of developing colors of the image forming apparatus 2 .

[0364] [7th Application Example] The image defect determination process in the seventh application example of the image processing device 10 will be described below.

[0365] In this application example, the job control unit 8b outputs four test output sheets 9 on which the monochromatic test images of different developed colors are formed in the test print process. Therefore, the test images g1 in this application example are monochromatic test images of different developed colors. In this case, the process of the color vector specifying unit 8e is not required.

[0366] Furthermore, in this application example, the periodicity judgment unit 8f, the noise point judgment unit 8h, and the pattern recognition unit 8g perform processing to determine the cause of the image defect using the first feature image g21, the second feature image g22, and the third feature image g23 corresponding to the monochrome test image.

[0367] In this application example, the periodicity determination unit 8f, the noise point determination unit 8h, and the pattern recognition unit 8g determine that one of the multiple imaging units 4x that corresponds to the color of the monochromatic test image is the cause of the singular portion Ps1. However, there are exceptions to this rule, such as when the cause of the singular portion Ps1 is determined to be something other than the imaging unit 4x.

[0368] Even when this application example is adopted, the CPU 80 can correctly determine the causes of the vertical streaks Ps11, horizontal streaks Ps12 and noise points Ps13 based on the monochrome test image read from the test output sheet 9 output by the image forming device 2.

[0369] [8th Application Example] The image defect determination process in the eighth application example of the image processing device 10 will be described below.

[0370] In this application example, the CPU 80 determines the cause of the image defect by performing other processing on the first characteristic image g21, the second characteristic image g22, or the third characteristic image g23, instead of the processing by the pattern recognition unit 8g.

[0371] For example, the CPU 80 in this application example determines the cause of the image defect by case classification based on one or more of the number of unique portions Ps1, the width or aspect ratio of the unique portions Ps1, and the level of the pixel values ​​of the unique portions Ps1.

[0372] [9th Application Example] The image defect determination process in the ninth application example of the image processing device 10 will be described below.

[0373] In this application example, the noise point determination unit 8h determines whether or not the cause of the noise point Ps13 is the abnormal development by using a learning model in the first cause determination process in step S302. In this case, the noise point determination unit 8h uses the feature parameters of the noise point Ps13 based on the third feature image g23 as input data, and applies the input data to the learning model to determine whether or not the cause of the noise point Ps13 is the abnormal development.

[0374] In this application example, the learning model in which the characteristic parameters are the input data is a model that has been trained in advance using samples of multiple sets of the characteristic parameters corresponding to the noise point Ps13, the cause of which is the abnormal development, as training data. [Explanation of symbols]

[0375] 1: Image reader 2: Image forming device 3: Sheet transport mechanism 4: Printing section 4x: Imaging section 10: Image processing device 41: Photoreceptor 42: Charging device 42a: Charging roller 43: Developing device 43a: Developing roller 43b:Developer tank 43c: Regulatory blade 44: Transcription device 46: Fixing device 430: Bias output circuit 440: Intermediate transfer belt (transfer body) D1: Main scanning direction D2: Sub-scanning direction Pd1: Original drawing part Ps1: Singular part Ps11: Vertical stripes Ps12: Horizontal stripes Ps13: Noise point Px1: Pixel of interest g1: Test image g11: First preprocessed image g12: Second preprocessed image g21: First feature image g22: Second feature image g23: Third feature image

Claims

1. An image processing method in which a processor determines a cause of an image defect based on a test image obtained through an image reading process for an output sheet of an image forming apparatus, comprising: The processor generates an extracted image in which noise points, which are one type of image defects, are extracted from the test image; determining a cause of the noise point using at least one of an edge strength of the noise point, a degree of flatness of the noise point, and a pixel value distribution in a transverse pixel column that is a pixel column that crosses the noise point in the extracted image; The processor uses the number of edges, which is the number of points where the change in pixel value in the transverse pixel row exceeds an acceptable range, to determine whether the cause of the noise point is abnormal development in which a defective toner image is developed on the surface of an image carrier by the developing unit of the image forming device.

2. 2. The image processing method of claim 1, wherein the processor derives as the number of edges the number of locations where a differential value of a pixel value in the transverse pixel column exceeds a predetermined first reference range, or the number of locations where a pixel value in the transverse pixel column crosses a reference value or a second reference range that is set based on the pixel values ​​of the transverse pixel column.

3. 3. The image processing method of claim 1, wherein the processor derives a first evaluation value according to the number of edges, a second evaluation value according to the edge strength, and a third evaluation value according to the degree of flattening, and uses the first evaluation value, the second evaluation value, and the third evaluation value to determine whether the noise points are caused by the abnormal development.

4. The image processing method according to claim 3, wherein the processor determines whether or not the noise point is caused by the abnormal development depending on whether or not an integrated value of the first evaluation value, the second evaluation value, and the third evaluation value exceeds a predetermined threshold.

5. The image processing method according to claim 3, wherein the processor determines whether or not the noise point is caused by the abnormal development depending on whether at least one of the first evaluation value, the second evaluation value, and the third evaluation value exceeds a corresponding threshold value.

6. The processor generating the extracted image comprises: generating a first preprocessed image by executing a first preprocessing including a main filter process for converting pixel values ​​of pixels of interest selected sequentially from the test image into converted values ​​obtained by a process for emphasizing a difference between a pixel value of a region of interest including the pixel of interest and pixel values ​​of two adjacent regions adjacent to both sides of the region of interest in a processing direction that is preset for the region of interest, with the horizontal direction of the test image as the processing direction; generating a second preprocessed image by performing a second preprocessing including the main filtering process with the vertical direction of the test image as the processing direction; The image processing method according to claim 1 , further comprising: generating, as the extracted image, an image in which singular areas consisting of one or more significant pixels in the first preprocessed image and the second preprocessed image, which are common to the first preprocessed image and the second preprocessed image, are extracted as the noise points.

7. The first pretreatment is generating first primary map data by performing the primary filtering process with the horizontal direction as the processing direction; generating lateral edge strength map data by performing edge enhancement filtering on the test image with the lateral direction as the processing direction and the region of interest and one of the two adjacent regions as targets; generating the first pre-processed image by correcting each pixel value of the first main map data with each pixel value of the corresponding horizontal edge strength map data; The second pretreatment is generating second primary map data by performing the primary filtering process with the vertical direction as the processing direction; generating vertical edge strength map data by performing the edge enhancement filter process on the test image with the horizontal direction as the processing direction and targeting the region of interest and the two adjacent regions; 7. The image processing method of claim 6, further comprising: generating the second preprocessed image by correcting each pixel value of the second main map data with each pixel value of the corresponding vertical edge strength map data.

8. generating the extracted image detecting point-like sheet noise contained in the sheet itself of the output sheet from the noise points in the extracted image generated based on the first preprocessed image and the second preprocessed image, and removing the sheet noise from the extracted image; The image processing method according to claim 6 or 7, wherein the processor determines a cause of the noise points based on the extracted image from which the sheet noise has been removed.

9. The image processing method described in claim 8, wherein the processor detects the sheet noise by performing a sheet noise pattern recognition process that uses a characteristic portion including the noise points in the extracted image as an input image and determines whether the input image is an image corresponding to the sheet noise by pattern recognition of the input image.

10. 10. The image processing method according to claim 9, wherein the sheet noise pattern recognition process is a process for determining whether or not the input image is an image corresponding to the sheet noise by a learning model that has been pre-trained using a plurality of sample images corresponding to the sheet noise as teacher data.

11. The image processing method according to claim 1 , further comprising the processor notifying the result of the determination of the cause of the noise points through a notification device.

12. The image processing method according to any one of claims 1 to 11, further comprising, when it is determined that the cause of the noise points is abnormal development in which a defective toner image is developed on the surface of an image carrier by a developing unit of the image forming device, outputting a correction command to the image forming device indicating to reduce a maximum potential difference between a dark potential on the surface of a photoconductor and a developing bias applied to a developing roller that develops an electrostatic latent image formed on the surface of the photoconductor into a toner image.

13. An image processing device comprising a processor for executing the image processing method according to any one of claims 1 to 12.

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